Safety-critical cyber-physical systems require rigorously verifiable specifications, yet natural language (NL) requirements introduce ambiguity and defects. While Metric Temporal Logic (MTL) can express complex real-time constraints, manual NL-to-MTL translation is labor-intensive and error-prone. Existing single-agent LLM approaches like TR2MTL struggle with semantic ambiguity and lack automated refinement mechanisms. This paper proposes DSVA (Deconstruct-Synthesize-Verify-Analyze), a multi-agent framework with diagnostic-driven iterative refinement. DSVA deploys specialized agents: Deconstruct parses NL into semantic sketches; Synthesize generates MTL formulae; Verify controls iteration via back-translation and similarity checking; and Analyze diagnoses failures to guide refinement. A phase-specific Retrieval-Augmented Generation (RAG) mechanism enhances contextual understanding. Evaluation on the TR2MTL dataset across four LLMs shows DSVA achieves 85.6% mean accuracy, with GPT-4 reaching 90.9%, a 17.99 percentage point improvement over the TR2MTL baseline (72.91%). Comprehensive ablation studies reveal synergistic component interactions: RAG contributes + 15.1% when combined with iteration, iterative refinement contributes +14.4% when combined with RAG, yielding a + 7.6pp synergy bonus beyond additive effects (14.3pp<inline-formula> <tex-math notation="LaTeX">$\rightarrow 21.9$ </tex-math></inline-formula>pp). Even the weakest full DSVA configuration (Gemini-2.5-flash: 75.8%) surpasses the baseline. The framework’s consistent cross-model performance gains validate its architectural benefits: specialized agent division of labor, automated iteration control, and diagnostic refinement, providing a potentially more reliable solution for automated requirements formalization in safety-critical systems.
The impacts of climate change on livelihoods and livestock systems in East Africa are significant. Efforts to bolster resilience will require a concerted focus on social equity to foster transformative adaptation. We integrate a feminist lens in a positive deviance approach to better understand gender relations in dairy producing communities in Kenya. We make theoretical and methodological contributions and suggest practical application to support locally led scaling approaches. Data was collected through 20 sex disaggregated focus group discussions (FGDs) and 10 key informant interviews (KII) with a total of 199 research participants. We focus on women's and men's participation in decision-making to assess gendered agency and labor in households, dairy specific activities, and the uptake of climate innovations. Evaluating these relations provides a better understanding of equity in dairy producing households who are at the forefront of climate adaptation. Women's and men's practices vary, and, through a positive deviance inquiry, we find the common patterns in those practices to characterize the referent group using thematic analyses. Our empirical findings demonstrate that referent group norms, relations and practices are, by and large, inequitable in agency and labor in dairy households underpinned by social norms. Positive deviant practices occur at differential rates in diverse geographies. We extended the concept of positive deviance to a relevant and urgent development agenda, transformative adaptation, that, to support resilience, must address root causes of vulnerability. We advocate for increased efforts to utilize positive deviance in future climate adaptation studies to inform practical and locally led strategies.
Vishwapranav P, Gowthaman N, Ganesan Pandian
et al.
Proton Exchange Membrane (PEM) electrolyzers are crucial components in hydrogen production systems, where they play a significant role in splitting water into hydrogen and oxygen. However, the performance and longevity of PEM electrolyzer are highly dependent on the condition and integrity of the individual stacks within the system. Regular monitoring and maintenance of these stacks are essential to ensure efficient operation and prevent system failures. Traditionally, this process is labor dependent, requires human inspection and impedance measurement of each stack, which is time-consuming, prone to human error, and may lead to delayed detection of anomalies. Thus, developing an automatic system that constantly acquires data from the cell stack with the integration of sensors (such as current and voltage) can significantly improve cell efficiency, economize cost as cells are made of materials such as platinum which is highly expensive, also preventing changes in torque set and significant time conservation. The main objective is to apply this automatic system to multi cell stack arrangement, where only the total cell voltage and current can be acquired through software but not individual cell’s values. This research introduces an automated system to address the challenges encountered in multi-cell stack configuration.
Septri Widiono, Jheni Juwita Sari Melia, Ketut Sukiyono
et al.
Land tenancy and labor relations are two agrarian institutions that shape the organization of agricultural activities at the production level and often influence production performance. This study explores the patterns of land tenancy and labor relations among rice-farming communities in Lahat Regency, South Sumatra, through the lens of the reciprocity economic perspective. Employing a qualitative case study approach, the research was conducted in four villages across the sub-districts of Tanjung Sakti Pumi and Lahat Selatan. Data were collected through in-depth interviews with the tenant farmers, landowners, extension officers, and community leaders. Findings reveal two dominant sharecropping systems: an equal 1:1 distribution and a saseh system with a 2:1 share. These informal and unwritten arrangements are based on kinship ties, mutual trust, and local norms. They provide stable land access for landless farmers and maintain production continuity, despite limited institutional support. Labor relations are equally embedded in cultural practices such as gotong royong (mutual aid), where families and neighbors contribute unpaid or semi-paid labor during peak agricultural periods. Compensation may include food, rice, or low wages, depending on the relationship and labor type. However, they reveal underlying tensions between tradition and social transformation, in which evolving reciprocity practices may increasingly function as mechanisms of surplus accumulation under market penetration. Theoretically, this study contributes to rural sociology by demonstrating how informal systems of tenancy and labor persist as rational and adaptive responses to socio-economic constraints, while also highlighting their potential vulnerability to erosion under agrarian pressures. The findings suggest that development policies should aim to reinforce these community-based systems to improve agricultural sustainability and social equity in rural Indonesia.
Value engineering (VE), as a conceptual approach and management technique, has allowed enterprises to capture value through mass production and market expansion during the industrial economic era. The VE method has enabled companies to produce products that meet user needs at a lower cost, leading to success. However, as the complexity of society and industry development increases, the lack of theoretical expansion in VE has limited its application in today’s more complex and macro management systems. With the development and evolution of vehicle–road collaborative intelligence, the intelligent automotive industry has become a complex system with multiple entities and interwoven values across different dimensions. Intelligent connected vehicles (ICVs), along with the external intelligent environment, will jointly participate in the realization of system functions. It is no longer sufficient to apply VE methods to analyze ICVs from a single product perspective. The pursuit of “maximizing value” is always the core driving force of industrial development. This study, building on the fundamental ideas of VE, expands and extends the connotation and theory of VE in three aspects: research objects, value dimensions, and associated entities, to adapt to the current situation. It also provides a new analysis process for the VE theory to better address systemic and complex issues. Taking the intelligent automotive industry as a case study, this study analyzes it based on the expanded VE theory. It considers not only the cost of system function realization and the product value of ICVs but also the external benefits of the system across different dimensions. The social value, user value, enterprise value are introduced in entity value analysis, and the relevant indicators are organized. This approach can better guide the collaboration and division of labor among multiple participating entities such as governments, enterprises, and users, achieving overall value maximization.
Ching-Hsi Tseng, Yu-Heng Hsieh, Wei-Qi Chen
et al.
Abstract As labor dispatch becomes increasingly widespread, enterprises face serious challenges such as résumé fraud, low background verification efficien-cy, and heightened data breach risks—especially under growing regulatory pressures. Existing solutions, such as centralized HR systems and third-party verification platforms, may improve efficiency to some extent, but still suffer from issues including a lack of transparency, susceptibility to data tampering, and cumbersome verification procedures. These limitations highlight a signifi-cant research gap in balancing efficiency, privacy, and trust. This study aims to develop a secure, automated, and regulation-compliant labor dispatch management system that enhances the credibility, efficiency, and privacy protection in résumé verification. To address these issues, we propose a blockchain-based labor dispatch system that integrates smart contracts for automated credential validation and incorporates Self-Sovereign Identity (SSI) to reinforce data ownership and control. Leveraging the immutability and decentralization of blockchain, the system ensures secure contract storage and auditability, while an access control mechanism effectively mitigates unauthorized data access risks. Our research involves the development of a prototype system, evaluated based on key performance indicators such as transaction processing time, verification latency, data integrity, and security. The contributions of this study include: (1) Credential certification of personal data ensures the authenticity of résumés, improving recruitment efficiency; (2) Secure contract storage on the blockchain satisfies both business confidentiality and legal compliance requirements; (3) A robust access control mechanism effectively safeguards sensitive personal data, reducing the risk of unauthorized access and data breaches; (4) The construction and performance evaluation of a blockchain-based labor dispatch system prototype addressing key operational metrics. Compared to existing technologies, the proposed model demonstrates signifi-cant advantages in trustworthiness, efficiency, and data protection and shows strong potential for practical deployment in highly regulated industry environ-ments.
Inequalities are inherent to capitalism. However, supply-chain production has introduced new dynamics in which workers situated in uneven labor regimes increasingly find themselves working alongside one another. On Mexico’s car assembly lines, autoworkers work next to logistics workers. The former belong to a residual workforce shaped by the historical triadic social contract between organized labor, the state, and companies; the latter are among the most precarious workers in the post-NAFTA era. This ethnographic study focuses on how a tiered labor system shapes the sociality between workforces and draws attention to the circulation of favors—asymmetrical exchanges of material and nonmaterial assistance that unintentionally arise from uneven working conditions. By delving into these exchanges, this article illuminates the ways in which gender and age intersect in reconfiguring a tiered labor system. It offers insights into the precariousness of labor flexibility by providing a glimpse into the dynamics on a factory shop floor. RESUMEN Las desigualdades son integrales al capitalismo. Sin embargo, la producción organizada en cadena de suministros ha introducido dinámicas en donde trabajadores situados en regímenes laborales desiguales se encuentran cada vez más trabajando juntos. En las cadenas de montaje de automóviles de México, los trabajadores del automóvil trabajan codo con codo con los trabajadores de logística. Los primeros pertenecen a una mano de obra conformada por el histórico contrato social entre sindicatos, el Estado y las empresas; los segundos se encuentran entre los trabajadores eventuales más precarios empleados en la economía de exportación. Este estudio etnográfico examina como un sistema laboral estratificado le da forma a la socialidad entre los trabajadores. El artículo se centra en los favores—intercambios asimétricos de ayuda material e inmaterial que surgen involuntariamente de las condiciones de trabajo. Al enfocarse en estos intercambios, el artículo ilumina las formas en que el género y la edad se entrecruzan en la reconfiguración de un sistema laboral estratificado. Al ofrecer una examinación de la dinámica laboral en una fábrica, el artículo proporciona una perspectiva de como se vive la precariedad de la flexibilidad laboral.
This article provides an overview of the concept of ‘algorithmic management’. This concept has played an important role as an organizing frame for empirical research seeking to demystify the role of labor platforms in intermediating paid work. More recently, this concept has helped shed light on the increasing use of computer algorithms to automate managerial tasks in conventional work settings. However, beyond platform work, most research is confined to warehousing, with only a few notable studies in manufacturing and retail. Moreover, most empirical investigations highlight the conditional nature of algorithmic management, with human managers retaining important functions. Only recently have studies begun to go beyond technical functions and consider how human elements (worker, manager, and technologist) shape such systems. Relatedly, the contingencies, moderations, and variations in algorithmic management have received insufficient consideration. These weaknesses result from a tendency to generalize from single case studies without adequately extending out from the case to theory, history, and geography, and not situating empirical research within a broader theoretically motivated research program. Workplace regime theory, with its focus on technology, power, and embeddedness, is presented as a remedy that enables algorithmic management research to account for variations while explaining regularities.
Software vulnerabilities pose a significant threat to computer systems because they can jeopardize the integrity of both software and hardware. The existing tools for detecting vulnerabilities are inadequate. Machine learning algorithms may struggle to interpret enormous datasets because of their limited ability to understand intricate linkages within high-dimensional data. Traditional procedures, on the other hand, take a long time and require a lot of manual labor. Furthermore, earlier deep-learning approaches failed to acquire adequate feature data. Self-attention mechanisms can process information across large distances, but they do not collect structural data. This work addresses the critical problem of inadequate vulnerability detection in software systems. We propose a novel method that combines self-attention with convolutional networks to enhance the detection of software vulnerabilities by capturing both localized, position-specific features and global, content-driven interactions. Our contribution lies in the integration of these methodologies to improve the precision and F1 score of vulnerability detection systems, achieving unprecedented results on complex Python datasets. In addition, we improve the self-attention approaches by changing the denominator to address the issue of excessive attention heads creating irrelevant disturbances. We assessed the effectiveness of this strategy using six complex Python vulnerability datasets obtained from GitHub. Our rigorous study and comparison of data with previous studies resulted in the most precise outcomes and F1 score (99%) ever attained by machine learning systems.
Elena A. Korol, Evgeniy N. Degaev, Dmitry S. Konyukhov
The mining industry is one of the key sectors of the Russian economy, supplying other industries with essential raw materials. However, this sector is characterized by harsh working conditions that may adversely affect workers' health. Exposure to harmful substances and significant physical workloads contribute to the development of occupational diseases. To ensure safety in production processes and protect the health of mining industry workers, it is necessary to conduct a special labor conditions assessment. This assessment allows for determining the level of harmfulness and hazard in workplaces, as well as developing measures to reduce the negative impact on workers' health. The purpose of this study is to assess dust concentration at the workplace of a crushing and screening plant operator as part of a special labor conditions evaluation. Dust concentration at the operator's workplace was measured using a standard gravimetric method. The testing was conducted in four stages and lasted 400 minutes, which is 83% of the total work shift duration. Data analysis revealed an exceedance of the permissible dust concentration by a factor of 1.28. The labor conditions class (subclass) was established as 3.1. It was found that the average dust concentrations varied by a factor of 3–4 across different testing stages due to the intensity and direction of air velocity at the production site. Based on the obtained data, dust concentrations at the workplace were predicted according to air velocity at the site, with an approximation accuracy of R2 = 0.95. It was determined that the maximum allowable air velocity at the site should not exceed 2.6 m/s. Using approximated data, it was forecasted that, in the absence of air movement, the dust concentration at the operator's workplace would remain at 0.5 mg/m³. To reduce dust concentration at the operator’s workplace, comprehensive measures to minimize dust generation at the crushing plant are necessary, including washing vehicle wheels, installing dust suppression systems, and replacing the open belt conveyor with a closed one. To prevent the development of occupational diseases, operators are advised to use personal respiratory, skin, and eye protection throughout the shift.
Shikder Saiful Islam, Saikat Ranjan Mondal, Joyanta Bir
et al.
Abstract This study investigated the production performance, household fish consumption, and commercial feasibility of prawn–carp–mola mixed gher farming system. Three treatments with different species compositions were compared: prawn + rohu, prawn + mola, and prawn + rohu + mola, each having different stocking densities. The results indicated that the integration of mola improved the utilization of feed protein by prawns. However, mola inclusion did not significantly affect the growth of prawn and rohu or the production system's cost. Mola inclusion led to a significant increase in the gross production, household consumption, and sale of mola, prawn, and rohu. It also increased gross returns, income above variable cost, and net returns to land, family labor, and management. In addition, the inclusion of mola significantly increased household consumption by increasing the intake of nutrient‐rich mola and overall fish consumption. This improvement in food consumption contributed to ensuring the nutritional requirements and food security of impoverished rural farmers, especially women and children. Consequently, the integration of small fish mola in prawn–carp gher farming systems is recommended as a beneficial practice for wider adoption, effectively addressing household nutrition security at the rural level and improving the livelihoods of farmers.
ABSTRACT: Chicken is a major source of dietary protein worldwide. The dispersion and movement of chickens constitute vital indicators of their health and status. This is especially evident in Taiwanese native chickens (TNCs), a local variety which is high in physical activity when healthy. Conventionally, the dispersion and movement of chicken flocks are observed in patrols. However, manual patrolling is laborious and time-consuming. Moreover, frequent patrols increase the risk of carrying pathogens into chicken farms. To address these issues, this study proposes an approach to develop an automatic warning system for anomalous dispersion and movement of chicken flocks in commercial chicken farms. Embendded systems were developed to acquire videos of chickens from overhead view in a chicken house, in which approximately 20,000 TNCs were raised for a period of 10 wk. Each video was 5-min in length. The videos were transmitted to a remote cloud server and were converted into images. A You Only Look Once—version 7 tiny (YOLOv7-tiny) object detection model was trained to detect chickens in the images. The dispersion of the chicken flocks in a 5-min long video was calculated using nearest neighbor index (NNI). The movement of the chicken flocks in a 5-min long video was quantified using simple online and real-time tracking algorithm (SORT). The normal ranges (i.e., 95% confidence intervals) of chicken dispersion and movement were established using an autoregressive integrated moving average (ARIMA) model and a seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) model, respectively. The system allows farmers to check up on the chicken farm only when the dispersion or movement values were not in the normal ranges. Thus, labor time can be saved and the risk of carrying pathogens into chicken farms can be reduced. The trained YOLOv7-tiny model achieved an average precision of 98.2% in chicken detection. SORT achieved a multiple object tracking accuracy of 95.3%. The ARIMA and SARIMAX achieved a mean absolute percentage error 3.71% and 13.39%, respectively, in forecasting dispersion and movement. The proposed approach can serve as a solution for automatic monitoring of anomalous chicken dispersion and movement in chicken farming, alerting farmers of potential health risks and environmental hazards in chicken farms.
Piotr Goliński, Patrycja Sobolewska, Barbara Stefańska
et al.
Maximizing annual pasture consumption without negatively impacting individual cow performance is of great importance in grass-based dairy and beef systems due to pasture being the most cost-effective nutrient source. However, the disadvantages of conventional and electric fencing include material and labor costs and increased manual labor. Virtual fencing has been developed and evaluated for almost two decades. The evolution of precision livestock farming, specifically virtual fencing, presents new opportunities for maximizing the utilization of available pasture land. Virtual fencing technology decreases the labor involved in physical fencing, provides greater adaptability to changes in pasture conditions, increases precision and efficiency, and offers additional flexibility in grazing management practices. However, that innovative technology should be further developed, and improvements should include decreasing the total costs of the system and increasing its application to other technological groups of ruminants, e.g., suckler cows with calves, increasing the efficiency of the system operation in large areas and a larger number of animals. Recent advancements in electronic communication and device (i.e., collar) design hold the potential to significantly enhance the effectiveness of the technology while also reducing costs. However, it is necessary to conduct a further evaluation to determine their utility in precision agricultural systems. This review paper aims to present an innovative concept of virtual fencing technology for pastures, compare currently available systems of this type, and indicate areas where further research and development should be carried out using Internet of Things (IoT) systems.
Unmanned aerial vehicle (UAV) swarms show a broad application prospect in the future. For most individuals of UAV swarms, their perception systems consist of onboard camera, gimbal and odometry. It is necessary to efficiently obtain accurate extrinsic parameters of camera-gimbal-odometry systems for UAV swarms. Traditional extrinsic calibration methods for camera-gimbal-odometry system often need manual assistances. For a large number of individuals of UAV swarms, it is time- and labor-consuming to complete the calibration using traditional methods. To tackle this problem, we propose an online extrinsic calibration algorithm and design a parallel procedure to realize an efficient calibration for UAV swarms. Without any dependence on manual operation and communication with ground station, the calibration for each individual is completed on onboard processor. Instead of manual labelling, a fast and accurate detection module is firstly built for samples auto-labeling. Facing the challenge of limited computing power of onboard processor, our auto-labeling algorithm shows a strong real-time capability and robustness. Then, an optimization module is developed to iteratively refine the extrinsic. Using multiple fixed-wing UAVs, online calibration experiments were conducted in larger-scale outdoor, and the results validated the feasibility of our method. Compared with some state-of-the-arts, the proposed extrinsic calibration method showed superior performance in terms of efficiency, accuracy and robustness.
To address the problems of existing drilling system like large mass, low mechanical drive efficiency and large space requirement, a hydraulic drilling system for marine geological survey is developed. The system uses an integrated hydraulic system to power its hoisting system, pipe handling system, subsea template deploying and control system. The gate type derrick, cylinder type drill pipe lifting system, elevation type hydraulic and pneumatic power tong and double driller integrated control system are used to meet the needs of fixed-point operations in ±1.5 m surge sea conditions. The system has been used 6 times with the Guangzhou Marine Geology Survey Ship No. 10, and has completed a drilling footage of about 1 000 m. The depth of the shallow sea bottom sampling core is up to 100 m. The average net time of drill pipe make-up and drilling operation is about 8 h/200 m. The working efficiency is increased by about 25% compared with the conventional drilling platform. The number of staffs is reduced from 7 to 3. The drilling rig reduces the on-site labor intensity and also significantly improves the sampling completion rate. The study results can provide references for the design of similar hydraulic geological drilling systems, and lay a foundation for subsequent geological survey and scientific research.
Chemical engineering, Petroleum refining. Petroleum products
Digital transformation involves changing and transforming the business model and adapting the company to market changes using different digital technologies. For companies to survive in the market and become competitive, it is necessary to adapt to the process of digital transformation that can be viewed as a change in all aspects of human life caused by digital technology. Nowadays, in the context of digital transformation, we can talk about the application of robots, artificial intelligence, 3D printing, drones, or so-called “industry 4.0”. The concept of Industry 4.0 requires digital transformation and networking of all functions inside and outside of the factory, where robots are used instead of workers in production lines. The development of robotization comes in all spheres of human life and is increasingly paying attention to their development, research and appliance. Robotization has an increasing impact on employment, so accordingly there is a need for new jobs, while today’s professions go to oblivion. Robots use sensory and control systems and artificial intelligence that leads them to independence in decision-making and work. Replacing workers in jobs that don’t seek creativity, other than routine work. Considering that the labor market will be significantly altered, the impact and automatization will be significantly different from sector to sector.